GtTR: Bayesian estimation of absolute tandem repeat copy number using sequence capture and high throughput sequencing

BMC Bioinformatics
Devika GanesamoorthyLachlan Coin

Abstract

Tandem repeats comprise significant proportion of the human genome including coding and regulatory regions. They are highly prone to repeat number variation and nucleotide mutation due to their repetitive and unstable nature, making them a major source of genomic variation between individuals. Despite recent advances in high throughput sequencing, analysis of tandem repeats in the context of complex diseases is still hindered by technical limitations. We report a novel targeted sequencing approach, which allows simultaneous analysis of hundreds of repeats. We developed a Bayesian algorithm, namely - GtTR - which combines information from a reference long-read dataset with a short read counting approach to genotype tandem repeats at population scale. PCR sizing analysis was used for validation. We used a PacBio long-read sequenced sample to generate a reference tandem repeat genotype dataset with on average 13% absolute deviation from PCR sizing results. Using this reference dataset GtTR generated estimates of VNTR copy number with accuracy within 95% high posterior density (HPD) intervals of 68 and 83% for capture sequence data and 200X WGS data respectively, improving to 87 and 94% with use of a PCR reference. We show that the...Continue Reading

References

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Datasets Mentioned

BETA
NA12879
NA12878
NA12890
PRJEB3381
SRX627421

Methods Mentioned

BETA
genotyping
PCR
electrophoresis
Illumina sequencing
Illumina
targeted capture sequencing

Software Mentioned

BLASR
VNTRtyper
MEM
binom R
PacBio
BWA
MixTaR
Multiphen
GtTR

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